Transforming data for rendering an insurability decision

ABSTRACT

Transformation of disparate data for use in rendering a decision involving a potentially insurable risk. An Extract, Transform, Load (ETL) process extracts the data and converts it from a plurality of formats into a standard format for processing. A heuristic engine inferentially processes the converted data to identify information relevant to the decision to be rendered. A consolidation and presentation engine generates presentable knowledge from the relevant information and then presents the knowledge to a decision-making entity for rendering the decision. And an optimization feedback process monitors one or more actions on the presented knowledge by the decision-making entity and adjusts one or more of the ETL process, the heuristic engine, and the consolidation and presentation engine as a function of the monitored actions.

BACKGROUND

Insurance companies typically determine insurance premiums and rates forapplicants based on the process of underwriting. In other words,underwriting involves measuring risk exposure and determining thepremium that needs to be charged to insure that risk. For example, lifeinsurance underwriting involves determining an individual's relativemortality and health insurance underwriting involves determining anindividual's relative morbidity. And as part of the underwriting processfor life or health insurance, medical underwriting and other factors(e.g., age and occupation) are used to examine the applicant's healthstatus.

Several sources of medical and nonmedical data exist for use in theunderwriting process. For example, a life or health insurance companyoften has internal records from previous policies, application data fora currently proposed policy, and data available from external sourcessuch as hospital and physician records, and prescription drug usageservices. The hospital and physician data can take the form ofElectronic Medical Records (EMR) or Patient Medical Information (PMI)files (including Attending Physician Statements (APS)). And commercialinspection companies make available to insurance companies a wide arrayof information from banking or financial information to driving history.To say this represents a river of data is an understatement. Theinsurance underwriter is faced with the task of drinking from the firehose. Although most, but not all, of these disparate sources aredeveloping emerging standards for this data, the standards for onesource often vary widely from the standards for another source becauseeach source is focused on satisfying a different business need.

Each insurance company has its own set of underwriting guidelines tohelp an underwriter determine whether or not the company should accept arisk and at what cost and with what restrictions. Once an applicant forinsurance authorizes the company's access to various pieces ofinformation, the underwriting process uses the information to evaluatethe risk of the applicant for insurance based on the type of coverageinvolved. Insurance companies sometimes use automated underwritingsystems to deliver an underwriting decision.

SUMMARY

Aspects of the invention translate and map data from a medical record orthe like into a structured database to enable the data to beunderwritten by either an electronic program or a human underwriter.

A method embodying aspects of the invention transforms disparate datafor use in rendering a decision involving a potentially insurable risk.The method includes receiving data, which is in a plurality of formats,from a plurality of sources. The data is extracted and converted intoone or more standard formats. The method also includes filtering theconverted data by relevancy to the decision to be rendered, generatingpresentable knowledge from the converted data, and presenting theknowledge to a decision-making entity for rendering the decision. Bymonitoring one or more actions on the presented knowledge by thedecision-making entity, the method can adjust one or more of steps as afunction of the monitored actions.

In an aspect, a method of structuring and transforming disparate datafor use in rendering a decision involving a potentially insurable riskincludes retrieving data from a first database and transforming theretrieved data into domain-specific information. Once transformed, theinformation, which relates to the potentially insurable risk, is storedin a second database. The method includes defining one or more relevancyfactors as a function of the decision to be rendered and assigning atleast one of the relevancy factors to at least a portion of theinformation stored in the second database. Additionally, the methodincludes providing an output of the second database with the assignedrelevancy factors to a decision-making entity for rendering thedecision.

In another aspect, a computer-readable medium stores computer-executableinstructions that, when executed, transform disparate data for use inrendering a decision involving a potentially insurable risk. Thecomputer-readable medium comprises, data from a plurality of sources andin a plurality of formats, an Extract, Transform, Load (ETL) process, aheuristic engine, a consolidation and presentation engine, and anoptimization feedback process. The ETL process extracts the data andconverts it from the plurality of formats into one or more standardformats. The heuristic engine inferentially processes the converted datato identify information relevant to the decision to be rendered. Theconsolidation and presentation engine generates presentable knowledgefrom the relevant information and then presents the knowledge to adecision-making entity for rendering the decision. And the optimizationfeedback process monitors one or more actions on the presented knowledgeby the decision-making entity and adjusts one or more of the ETLprocess, the heuristic engine, and the consolidation and presentationengine as a function of the monitored actions.

In yet another aspect, a system includes a memory storing disparate datarelating to a potentially insurable risk. A computer executes a processfor extracting at least a portion of the stored data and transformingthe extracted data from a plurality of formats into a standardizedformat. The memory then stores the transformed data in the standardizedformat. The computer executes a heuristic engine for analyzing thetransformed data for relevancy to a decision to be rendered involvingthe potentially insurable risk. Moreover, the heuristic engine assignsone or more relevancy factors to the analyzed data. In addition, adisplay displays an output including the assigned relevancy factors to adecision-making entity for rendering the decision.

In an aspect of the invention, an automated system is capable ofinterpreting medical conditions presented in a structured medical recordinto one of a plurality of limited underwriting impairments. Theautomated system is user-configurable to include more or fewerunderwriting impairments. And the automated system is user-configurableto enable modification of the medical condition mappings intounderwriting impairments. The automated system includes the capabilityto translate, interpret, and map a known medical condition based on oneor more factors including, but not limited to: medical condition name;medical condition code (e.g., CPT4, ICD9, ICD10, etc.); medicationsassigned; treatment regimens; age; gender; and so forth.

In another aspect, the automated system receives its input data fromvarious sources such that the data received is in a structured dataformat capable of being interpreted by an automated system.

In yet another aspect, the automated system produces a structured dataoutput consisting of at least one of the following: an underwritingmedical condition; a severity indication; a recommended action; or anindication that the medical condition is referred to a human tocorrectly map the medical condition to an underwriting impairment.

In yet another aspect of the present invention, the output of theautomated system is an input to an automated system or as input to ahuman for the actual process of underwriting the individual underconsideration.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Other features will be in part apparent and in part pointed outhereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary block diagram illustrating a system fortransforming medical and other data according to an embodiment of theinvention.

FIG. 2 is an exemplary block diagram illustrating a system fortransforming medical and other data according to another embodiment ofthe invention.

FIG. 3 is an exemplary block diagram illustrating alternative datasources to the system of FIGS. 1 and 2.

FIG. 4 is an exemplary flow diagram illustrating operation of the systemof FIGS. 1 and 2.

FIG. 5 is an exemplary flow diagram illustrating operation of aconsolidation and presentation engine of the system of FIGS. 1 and 2.

FIG. 6 is a block diagram illustrating an example of a suitablecomputing system environment in which aspects of the invention may beimplemented.

Corresponding reference characters indicate corresponding partsthroughout the drawings.

DETAILED DESCRIPTION

Referring now to the figures, aspects of the present invention translateand map information about an insurance applicant into a structureddatabase. This enables the information to be more effectively andefficiently underwritten by either an electronic program or a humanunderwriter. In one embodiment, a computer system, generally indicatedat 100, receives information, such as data stored in an external datadatabase 102, and creates structured data that fits into major“underwritten” sections (e.g., cardiovascular disease). The structureddata is preferably used for further underwriting evaluation, either byan automated system or by a human underwriter.

As an example, the data stored in the external data database 102comprises data from electronic medical records (EMRs). This externaldata can be from several sources and in varying formats. The system 100evaluates each EMR, for example, to identify relevant information and totranslate the identified information. In this regard, system 100 usesindustry-wide classifications, performs lexical analysis, accessesopen-source or propriety databases (e.g., databases provided by areinsurance company), or the like. The EMR data input to system 100often includes fields such as medical condition name, medical conditioncode, medications assigned, treatment regimens, age, gender, and so on.

As another example, a suitable source of information is a continuity ofcare record (CCR). Those skilled in the art are familiar with CCRstandards for creation of electronic summaries of patient health. TheCCR provides a means for a healthcare practitioner, system, or settingto aggregate pertinent data about a patient and forward it to anotherpractitioner, system, or setting to support the patient's continuity ofcare. For example, a typical CCR includes a summary of the patient'shealth status (e.g., problems, medications, allergies, lab results,procedures) and basic information about insurance, advance directives,care documentation, and care plan recommendations. The CCR is not an EMRor electronic health record (EHR) but it often contains some of the samedata as an EMR or EHR. A continuity of care document (CCD) is a CCRcreated under the Clinical Document Architecture (CDA) standard.

Aspects of the invention also relate to creating structured data fromnon-traditional records sources such as data from social networks andfrom internet datamarts instead of or in addition to EMR, EHR, CCR,and/or CCD data or the like.

An underwriting impairment typically defines factors that tend toincrease an individual's risk above that which is normal. Underwritingmanuals define one or more underwriting impairments or underwritingimpairment groups. Information in the underwriting impairment maydefine, for example, the individual's relative mortality, morbidity,and/or longevity. Although described in the context of life or healthunderwriting, it is to be understood that aspects of the invention alsoapply to disability, long term care, and other forms of insuranceunderwriting.

As shown in FIG. 1, computer system 100 permits selection and mapping oftranslated external data from database 102 to a structured database. Theexternal data stored in database 102 includes, for example,applicant-provided data, financial sources data, motor vehicle recordsdata, other non-medical sources data, electronic medical records data,electronic health records data, continuity of care records or documentsdata, prescription data, and other medical sources data.

The system 100 first extracts relevant information from the externaldata and then converts the extracted data into standard formats forprocessing. In one embodiment, system 100 weighs, filters, or otherwisedeems information to be more or less relevant based on factors such assource, type, age of data, covariance with other factors, etc. And theresulting structured data preferably contains fields such as anunderwriting medical condition, a severity indication, a recommendedaction, and/or an indication that further manual review is desired orrequired.

In one embodiment of system 100, the application programs 36 (see FIG.6) include a plurality of processes that when executed by system 100filter the structured data by relevancy and mine the data for valuableinformation. The processes further convert this information intoknowledge, namely, information that is particularly useful in theunderwriting process. FIG. 1 shows at least one knowledge engineeringprocess, generally indicated process 104 for determining which of therelevant information is actually usable in the underwriting process.Preferably, the process 104 employs experience studies, feedback, etc.to create and apply a knowledge model to the data. In addition, one ormore extract, transform, load (ETL) processes and one or more datamining processes, generally indicated process 106, filter the structureddata by relevancy and mine the data for valuable information. The resultof these highly specialized processes 104, 106 is a relatively largestaging area repository 108 of potentially usable data concerning theapplicant.

At least one heuristic engine 110 analyzes staged data stored in therepository 108. In particular, the heuristic engine 110 compares thedata against a proprietary database 112 representing a lexicon ofphrases, synonyms, ICD 10 codes, etc. and the covariances of the dataitems. Moreover, engine 110 assigns relevancy weightings for lifeunderwriting or for health underwriting. The output of heuristic engine110 is a refined, filtered collection of information pertinent to theunderwriting process stored in an underwriting information database 114.

In one embodiment, heuristic engine 110 executes a Markov Chaining MonteCarlo (MCMC) algorithm. Those skilled in the art are familiar withalgorithms of this type for use in predictive modeling. Aspects of thepresent invention utilize the MCMC methodologies to infer riskassessment relationships in seemingly unrelated data from disparatesources.

At least one consolidation and presentation engine 116 presents thestructured output of heuristic engine 110 in a form more directly usablefor underwriting (either manual or automated or both). Moreover, theconsolidation and presentation engine 116 offers a drill-downcapability, described below, to further underwriting information storedin a database 114. In this manner, engine 116 outputs scenario andapplicant-specific information as well as reference statisticsparticularly useful in the underwriting process.

Referring further to FIG. 1, system 100 includes a visual tool thatenables a user, such as an underwriter 118, to view the informationoutput from heuristic engine 110 as well as the information's underlyingfactors. Moreover, the visual tool enables the underwriter 118 access tothe information in the underwriting information database 114. In oneembodiment, the visual tool comprises a dashboard of consolidatedsummary information displayed on a display of a computer 120. Theunderwriter 118, generally considered the decision maker in underwritingscenarios, renders his or her decision based on the summary information.Typically, underwriter 118 is a trained professional who evaluates thepresented data and makes a decision to approve the application at aspecific rating for the policy, to decline the application, or torequest more information. In an alternative embodiment, the computer 120executes automated underwriting processes in addition to or instead ofmanual underwriting by underwriter 118. In the absence of a humanunderwriter, computer 120 constitutes the underwriter in thisalternative embodiment.

In an embodiment, a feedback system based on the consumption ormodification of the structured data is used to refine and adjust theselection, translation, and/or mapping of data to the structureddatabase. Moreover, the feedback process monitors underwriter actionsand results and alters previous operations via feedback loops. Forexample, the actions of each individual underwriter 118 are closelyobserved using an optimization technique, such as an “Ant ColonyOptimization” technique executed at process 122. The process 122 inferscollective information from the repeated and combined actions ofindependent individuals and adjusts the dashboard of summary informationdisplayed at computer 120 accordingly.

FIG. 2 illustrates an alternative embodiment of the invention. As shownin FIG. 2, computer system 100 permits selection and mapping oftranslated external data stored in database 102 to a structureddatabase. The external data 102 includes, for example,applicant-provided data 202, financial sources data 204, electronicmedical records data 206, prescription data 208, and other medicalsources data 210 (including but not limited to, for example, continuityof care records data). In addition, external data database 102 includescomplex data from non-EMR sources such as social network data 212 andinternet datamart data 214. The different types of external dataincluded in the external data database 102 can be stored in one or moredatabase structures.

Advantageously, extracting information from multiple data sourcesprovides the benefit of network theory. In this regard, the strength ofa network is the usual fault tolerance (e.g., random hits can take outas many as 80% of the locations while retaining functionality). But theweakness of a network is the vulnerability to catastrophe (e.g.,targeted hits take out very few locations but cause chaos). Thegovernment sponsored movement towards more integration of medical andrelated information into personal medical records is countered to someextent by another regulatory initiative concerning privacy issues. Thegoals are at times in conflict and the posture regarding whatinformation is fair game for risk assessments is in a state of flux.Embodiments of the invention use network theory to adjust processingcenters for high efficiency of data processing and embracing of datadeemed relevant, ethical, and legal to use, yet reduce the vulnerabilityto any specific data source or selection criterion as perspectiveschange.

The system 100 preferably uses inferential analysis to extract usefulinformation from the external data. Those skilled in the art arefamiliar with computational methods such as predictive modeling,Bayesian inference, genetic algorithms, and the like for performinginferential analysis. The system 100 first extracts relevant informationfrom external data stored in database 102 and then converts theextracted data into a standard format for processing. In one embodiment,system 100 weighs, filters, or otherwise deems information to be more orless relevant based on factors such as source, type, age of data,covariance with other factors, etc. And the resulting structured datapreferably contains fields such as an underwriting medical condition, aseverity indication, a recommended action, and/or an indication thatfurther manual review is desired or required.

Similar to the embodiment of FIG. 1, application programs 36 (see FIG.6) include a plurality of processes that when executed by system 100filter the structured data by relevancy and mine the data for valuableinformation. The processes further convert this information intoknowledge, namely, information that is particularly useful in theunderwriting process. FIG. 2 shows a plurality of processes, such asknowledge engineering process 104, heuristic engine 110, andconsolidation and presentation engine 116. Moreover, FIG. 2 illustratesprocess 106 as one or more ETL processes 218 and one or more data miningprocesses 220. The processes 104, 106 (including 218, 220), 110, 116 arecollectively referred to as inference engines.

The engine 116 transforms information from various sources into a formmore directly usable for underwriting (either manual or automated orboth). Traditional information sources include applicant-provided data202, financial sources data 204, electronic medical records data 206,prescription data 208, and other medical sources data 210. Thetraditional sources of data, although different from each other in manyrespects, share a general perspective on the health or financial stateof the applicant.

A person who recently underwent major surgery, or who is in financialdistress, for example, is more likely to have a greater mortality orhealth insurance risk than another person with a secure, comfortablyhigh income, low debt, good family history of longevity, lower (but nottoo low) blood pressure and cholesterol levels, and a body mass index(BMI) and other physical characteristics in the more desirable ranges.

The consolidation and presentation engine 116 generates succinct, highusable information from the transformed data stored in underwritinginformation database 114. For example, engine 116 summarizes datarepresenting years of biometric levels into a moving weighted average.In another embodiment, engine 116 presents a chart of the metricssuperimposed on a background chart of those metrics for the normal rangeof individuals of similar age, gender, smoker status, and other keyunderwriting criteria. Similarly, instead of data representing years ofprescriptions, engine 116 presents a listing of the distinctprescriptions, and an indication of dosage levels (and increasing ordecreasing trends), periods of noncompliance, and other key indicatorsto flag possible interactions between prescriptions or possible misuseof them.

In an alternative embodiment, engine 116 may be configured to operate onnon-traditional information, such as social network data 212 andinternet datamart data 214. Vast amounts of data on our personallifestyle habits have been collected and stored in various datamarts.And people contribute to the collective knowledge by voluntaryparticipation in social networks. Referring further to FIG. 2, if thetraditional sources form a river, the social networks data 212 andassociated datamarts data 214 (e.g., specialty companies that harvestdata about us from myriad sources) form a sea of data. If processedeffectively, this lifestyle data can be a useful prognosticator offuture, rather than just current morbidity and mortality concerns. Andthis data could add significantly to the total picture of insurability.

For example, assume person X lives in a neighborhood where the crimerate is very low, jogging trails are plentiful, and the local cultureencourages walking rather than driving. Further, X has high equity inher home, a graduate degree in a high paying but relatively low stressprofession, and does not subscribe to the premium cable televisionpackage (thus, is not a couch potato). Instead, she subscribes to apopular magazine for serious runners, writes a blog on organic foods,buys mostly whole grains and vegetables on her loyalty card at thegrocery chain, wrote a review of her cardiac monitoring wristwatch on anonline retailer's website, regularly attends a yoga class at her localfitness center, and recently posted pictures to her social networkprofile showing her grandfather's 100th birthday celebration. This mixof data could provide a favorable indicator of X living for a longertime than an otherwise similar individual who posts, for example,pictures from a party at a local tavern, blogs about the tastedifferences of cigar A versus cigar B, and comments about recentlybuying a new muscle car to race at the local stock car track.

Today's life or health insurance underwriter is a magnificent humaninference engine capable of assimilating information about an applicantand assigning appropriate risk classifications that drive the issuanceof profitable, yet equitable, rates for insurance coverage. But it is nolonger humanly possible (and certainly not cost effective) for anunderwriter to study all of the data available for an applicant for alife or health insurance policy. Aspects of this invention embody atransformation from vast amounts of data to usable nuggets ofinformation.

Referring further to FIG. 2, some data, especially data from the moretraditional sources, are run through tailored ETL processes 218 toconsolidate them into the common repository 108 for further study. Inone embodiment, a tailored ETL process 218 corresponds to each source ofexternal data 102. In other words, each ETL process 218 is specific tothe domain, or source, of the data. The ETL process extracts informationfrom its corresponding data source without regard to each dataorganization/format and transforms, or converts, the extracted data to astandard format. This permits consolidation and loading of the data intorepository 108.

Other data, such as social networks data 212 and datamarts data 214, canbe so voluminous as to make this more direct type of mapping processunfeasible in realistic timeframes. This other data 212, 214 isprocessed by, for example, advanced statistical methodologies, i.e.,data mining processes 220. In one embodiment, data mining processes 220comprise predictive modeling and similar techniques to “follow the breadcrumbs” and detect covariance relationships between seeminglyindependent pieces of data.

The system 100 also operates on internal information stored in adatabase 222 and converts the raw data into a form more directly usablefor underwriting. For example, a reinsurance company has a perspectiveon underwriting practices and mortality results across many companiesand maintains its own repository of extensive data, indicated generallyas internal data database 222. The knowledge engineering process 104with expert human underwriters, actuaries, and other insuranceprofessionals continually refines this valuable source of proprietaryinformation.

Embodiments of the invention involve the storage of vast amounts ofdata, such as external data in database 102 (both traditional andnon-traditional sources), internal data in database 222, lexicon andrelevancy weights data in database 112, staged data in repository 108,and underwriting information in database 114. Although referred to asstored in databases or repositories, it is to be understood that thedata can be stored, organized, and maintained in myriad forms.

In the embodiment of FIG. 2, heuristic engine 110 analyzes the stageddata in repository 108. In particular, heuristic engine 110 compares thedata against the proprietary database 112 representing a lexicon ofphrases, synonyms, ICD 10 codes, etc. and the covariances of the dataitems. Moreover, engine 110 assigns relevancy weightings for lifeunderwriting or for health underwriting.

For example, the relevancy of an item such as back pain might be oflittle consequence for a life application but of much higher relevancefor health underwriting. And in another example, a hearing loss might beunimportant for most life applicants, yet rise in importanceconsiderably if the applicant is employed as a traffic guard.

The result of this proprietary filtering process is a refined collectionof information pertinent to life (or health, if that is the coveragesought) underwriting. Even this may be too much information for anunderwriter to efficiently absorb. For example, BMI and blood pressureand cholesterol levels for the past 30 years is likely to be moreinformation than underwriter 118 can effectively process. Similarly,information about monthly prescription medications for the past 15 yearsis likely too much data to be usable. The consolidation and presentationengine 116 transforms this information into a form more directly usableby the underwriter.

Referring further to FIG. 2, system 100 includes a visual tool thatenables underwriter 118 access to the information in the underwritinginformation database 114. And in an embodiment, a feedback system basedon the consumption or modification of the structured data is used torefine and adjust the selection, translation, and/or mapping of data tothe structured database. Moreover, the feedback process monitorsunderwriter actions and results and alters previous operations viafeedback loops. For example, the actions of each individual underwriter118 are closely observed using an optimization technique, such as an“Ant Colony Optimization” technique executed at process 122. The process122 infers collective information from the repeated and combined actionsof independent individuals and adjusts the dashboard of summaryinformation displayed at computer 120 accordingly.

For example, if multiple underwriters 118 tend to drill down on themedications and consult a dictionary for potential drug interactions,this becomes part of the collective knowledge of the inference engines104, 218, 220, 110, and/or 116. Future summary dashboards reflect thisfeedback by including this specific information, which savesunderwriting time on future applications. Likewise, the informationvalue is quickly scored by underwriter 118 and information used lessfrequently loses prominence, or real estate, on the summary screen. Inthis manner, aspects of the invention improve at providing theinformation wanted and not providing the extraneous data that obscures acost and time effective decision on the part of the human expert.Likewise, if the information in the refined repository of underwritinginformation 114 is not sufficient, the inference engines 104, 218, 220,110, and/or 116 may be adjusted accordingly.

Aspects of the invention provide all that is necessary and sufficientwithout the distraction of that which is superfluous. And, in oneembodiment, the invention comprises an underwriting appliance that hasseveral alternative physical forms. Referring now to FIG. 3, a cedingcompany can choose a stand-alone, proprietary terminal linked to areinsurer for maximum efficiency of this operation, or one of variousother options that permit a balance of functionality and ease-of-useversus ceding company internal data security concerns.

For example, in FIG. 3, an underwriting appliance 302 (i.e., a hardwarearrangement) comprises a dedicated terminal to the reinsurer, such ascomputer 120, with a specialized keyboard and hot keys to most commonfunctions. This has no connection to ceding company IT operations and,thus, is ideal for situations where security is a prime concern of theceding company. In an alternative underwriting appliance 304, the cedingcompany underwriter 118 uses a personal computer, such as computer 120,with a reinsurer specialized keypad 306 attached via the USB port or thelike. This permits normal access to the ceding company network andperipherals. Moreover, the appliance 304 is convenient for a largeunderwriting department and for situations involving remoteunderwriters. Another alternative underwriting appliance 308 includes aspecialized tablet 310 (e.g., an iPad) for use by a highly mobileunderwriter 118. In yet another alternative underwriting appliance 312,the ceding company underwriter 118 uses a personal computer, such ascomputer 120, with no attached hardware. A relatively small, on-screenkeyboard 314 is available to provide the hot key operations. Thispermits normal access to the ceding company network and peripherals.Similar to the underwriting appliance 304, the appliance 312 isconvenient for a large underwriting department and for situationsinvolving remote underwriters. Preferred hot keys on the specializedinput device include an automatic login to the reinsurer's underwritingappliance via a secure internet site, and various views (arrangements ofcontent and form) for differing benefit underwriting perspectives suchas Life, Health, Disability Income, Long Term Care, etc. as well asdirect access to the reinsurer's underwriting manual. Additionalfeatures include the ability to submit the application to the reinsurer.

FIG. 4 illustrates an exemplary, non-limiting process in accordance withan embodiment of the invention. In operation, computer system 100receives external data 102 at 402 for selection and mapping to astructured database. As set forth above, external data 102 includes datafrom multiple sources in a variety of formats, such asapplicant-provided data, financial sources data, electronic medicalrecords data, prescription data, and other medical sources data. At 406,system 100 first extracts relevant information from external data 102and then converts the extracted data into standard formats forprocessing. In one embodiment, system 100 executes process 104 and/orprocess 106 to perform the data extraction and conversion. The system100 stores the extracted data in staging area repository 108.

Proceeding to 408, system 100 executes heuristic engine 110 to weigh,filter, or otherwise deem information to be more or less relevant basedon factors such as source, type, age of data, covariance with otherfactors, etc. And the resulting structured data preferably containsfields such as an underwriting medical condition, a severity indication,a recommended action, and/or an indication that further manual review isdesired or required. Moreover, engine 110 assigns relevancy weightingsfor life underwriting or for health underwriting. The output ofheuristic engine 110 is a refined, filtered collection of informationpertinent to the underwriting process stored in underwriting informationdatabase 114.

At 410, the consolidation and presentation engine 116 of system 100converts this information into knowledge, namely, information that isparticularly useful in the underwriting process. As a result, engine 116presents the structured output of heuristic engine 110, i.e., theunderwriting information 114, in a form more directly usable forunderwriting (either manual or automated or both). The system 100includes a visual tool that enables underwriter 118 to view the summaryinformation output from heuristic engine 110 as well as theinformation's underlying factors. For example, computer 120 displays adashboard of consolidated summary information to underwriter 118.

Feedback at 412 based on the consumption or modification of thestructured data refines and adjusts the selection, translation, and/ormapping of data to the structured database. Moreover, the feedbackprocess monitors underwriter actions and results and alters previousoperations via feedback loops 414.

FIG. 5 provides a logical overview of the operation of consolidation andpresentation engine 116 at step 410 of FIG. 4 according to an embodimentof the invention. Beginning at 502, engine 116 receives the extractedinformation stored in underwriting information database 114. At 504,engine 116 executes a decision operation to determine whether thereceived information has a relatively high degree of relevance to theparticular underwriting scenario. If so, engine 116 proceeds to 506 fora determination of whether the information is already in a concise,usable form. And if the information is relevant and concise, engine 116determines at 508 whether the information is suitable for top leveldisplay.

On the other hand, if engine 116 determines at 504 that the receivedinformation does not have a sufficiently high degree of relevance to theparticular underwriting scenario, operation proceeds to 510. At 510,engine 116 determines whether the information would have a relativelyhigh degree of relevance if combined with other data. If not, theinformation engine 116 disregards the data at 512. But if theinformation would be sufficiently relevant if combined, engine 116combines the data at 514 and proceeds to 506.

If engine 116 determines at 506 that the relevant information is notalready in a concise, usable form, operation proceeds to 516. The engine116 builds a summary at 516 such that the information is more usable inthe underwriting process and then proceeds to 508 for a decision onwhether the summarized information is suitable for top level display.

The engine 116 causes information suitable for top level display to bedisplayed at 518 and otherwise stores the information at 520 so that itis available for display when underwriter 118 drills down for furtherdetail. The consolidation and presentation engine 116 offers thedrill-down capability to permit underwriter 118 to access furtherunderwriting information stored in a database 114. In other words, therelevance and nature of certain information may not warrant topimmediate display but underwriter 118 can access the information if heor she deems it of importance to the underwriting decision. In thismanner, engine 116 outputs scenario and applicant-specific informationparticularly useful in the underwriting process and provides the abilityto drill down on additional underwriting information.

As described above, system 100 preferably uses inferential analysis toextract useful information from external data 102. The system 100 firstextracts relevant information from external data 102 and then convertsthe extracted data into a standard format for processing. In oneembodiment, system 100 weighs, filters, or otherwise deems informationto be more or less relevant based on factors such as source, type, ageof data, covariance with other factors, etc.

Those skilled in the art are familiar with computational methods such aspredictive modeling, Bayesian inference, genetic algorithms,nature-inspired metaheuristic algorithms and the like suitable forperforming inferential analysis in the form of knowledge engineeringprocess 218, data mining process 220, heuristic engine 110,consolidation and presentation engine 116, and/or optimization process122. Advantageously, system 100 according to an embodiment of theinvention utilizes a combination of processes to weigh, filter, orotherwise deems information to be more or less relevant and to optimizethe processes. This combination of processes permits system 100 toidentify ways in which the processes are vulnerable to minute changes indata granularity, starting assumptions or on covariances between majorand obscure variables, and adjust accordingly.

In the past, underwriters, actuaries, economists, and computerscientists built sophisticated mathematical models based upon prevailingreductionist theory, and expected the world to conform to them. Theywere dismayed when the world did not adhere and behave the way it was“supposed” to behave. In contrast, aspects of the present invention addthe power of inductive reasoning techniques, which learn from the dataand the way it is utilized. These adaptive aspects of the inventionprovide a unique advantage for the increasingly dynamic nature of riskassessment for life, health, disability income, long term care, andother types of insurance applications.

Aspects of the invention utilize complexity science tools andtechniques, including predictive modeling, network theory, deterministicchaos, behavioral economics, fractal geometry, genetic algorithms, andcellular automata. These aspects represent a marked departure from theclassical, more deterministic approach to risk assessment.

For example, embodiments of the invention involve the storage of vastamounts of data, such as external data in database 102 (both traditionaland non-traditional sources), internal data in database 222, lexicon andrelevancy weights data in database 112, staged data in repository 108,and underwriting information in database 114. Although vast, the data isreadily accessible when needed, and the data models are highly scalable.In an embodiment, fractal geometry techniques help achieve scalabilityof interrelationship inferences beyond currently popular methods bytaking advantage of self-similarities in the data.

In another example, genetic algorithms, namely, nature-inspiredmetaheuristic algorithms and the like, provide solutions to optimizationand search problems in inferential analysis processes. Many riskassessment problems have no clear deterministic solution, and anexhaustive search is beyond computational capabilities. In a situationin which the number of variables (e.g., gender, age, height, weight,systolic and diastolic blood pressure readings, low and high densitycholesterol readings, etc.) is large and the covariances of variables(such as diabetes plus high blood pressure plus obesity) can lead tocomplex interactions, system 100 in one embodiment uses one or moregenetic algorithms to simulate emergent phenomena from the interactionsof simpler, complex adaptive agents. An example of very simple agentsinteracting in complex ways would be the operation of an ant colony. Anant placed on a tabletop moves aimlessly but an ant colony is capable ofcomplex behaviors even without a designated leader. In an analogousmanner, ant colony optimizations, bee colony algorithms, and othermodeling techniques based on the complex interactions of simple agentsto solve problems not solvable with classic deterministic methods.

These nature-inspired metaheuristic algorithms are suited to observe thehuman actions of the underwriters as they utilize system 100. Thedashboard output generated on computer 120 by consolidation andpresentation engine 116 presents the information generally thought to beof the most interest to the human underwriter 118, with drill-downcapability to get more granular or detailed information as desired. Thefeedback process monitors how often the various primary items areclicked for more information, and which items are ignored, or used lessfrequently. It will then spawn simulations to infer how the futuredashboard arrangement can be changed to improve the user experience. Thedrill-down process also provides feedback to the collection andfiltering routines (e.g., processes 104, 106) to ensure that desiredinformation is collected and made more prominent. In a similar manner,ignored information no longer takes up valuable screen real estate (orin an extreme case, is no longer collected). It is contemplated thatprocesses can evolve; and the continual application of scoringmechanisms to determine the “fittest” aspects of the process, coupledwith the deliberately induced element of mutations (experimentalfeatures) can help system 100 to adapt to the changing scene of riskassessment in a manner superior to classical, more static, processes.

Moreover, it is contemplated that cellular automata principles can add anew dimension to genetic algorithm simulations for feedback andself-adjustment of the collection, filtering, relevancy, andpresentation engine processes.

Embodiments of the present invention may comprise a special purpose orgeneral purpose computer including a variety of computer hardware, asdescribed in greater detail below.

Embodiments within the scope of the present invention also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage, or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code means in the form of computer-executableinstructions or data structures and that can be accessed by a generalpurpose or special purpose computer. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as acomputer-readable medium. Thus, any such a connection is properly termeda computer-readable medium. Combinations of the above should also beincluded within the scope of computer-readable media.Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions.

FIG. 6 and the following discussion are intended to provide a brief,general description of a suitable computing environment in which aspectsof the invention may be implemented. Although not required, aspects ofthe invention will be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by computers in network environments. Generally, programmodules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Computer-executable instructions, associated datastructures, and program modules represent examples of the program codemeans for executing steps of the methods disclosed herein. Theparticular sequence of such executable instructions or associated datastructures represent examples of corresponding acts for implementing thefunctions described in such steps.

Those skilled in the art will appreciate that aspects of the inventionmay be practiced in network computing environments with many types ofcomputer system configurations, including personal computers, hand-helddevices, multi-processor systems, microprocessor-based or programmableconsumer electronics, network PCs, minicomputers, mainframe computers,and the like. Aspects of the invention may also be practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination of hardwired or wirelesslinks) through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

With reference to FIG. 6, an exemplary system for implementing aspectsof the invention includes a general purpose computing device in the formof a conventional computer 20, including a processing unit 21, a systemmemory 22, and a system bus 23 that couples various system componentsincluding the system memory 22 to the processing unit 21. The system bus23 may be any of several types of bus structures including a memory busor memory controller, a peripheral bus, and a local bus using any of avariety of bus architectures. The system memory includes read onlymemory (ROM) 24 and random access memory (RAM) 25. A basic input/outputsystem (BIOS) 26, containing the basic routines that help transferinformation between elements within the computer 20, such as duringstart-up, may be stored in ROM 24.

The computer 20 may also include a magnetic hard disk drive 27 forreading from and writing to a magnetic hard disk 39, a magnetic diskdrive 28 for reading from or writing to a removable magnetic disk 29,and an optical disk drive 30 for reading from or writing to removableoptical disk 31 such as a CD-ROM or other optical media. The magnetichard disk drive 27, magnetic disk drive 28, and optical disk drive 30are connected to the system bus 23 by a hard disk drive interface 32, amagnetic disk drive-interface 33, and an optical drive interface 34,respectively. The drives and their associated computer-readable mediaprovide nonvolatile storage of computer-executable instructions, datastructures, program modules, and other data for the computer 20.Although the exemplary environment described herein employs a magnetichard disk 39, a removable magnetic disk 29, and a removable optical disk31, other types of computer readable media for storing data can be used,including magnetic cassettes, flash memory cards, digital video disks,Bernoulli cartridges, RAMs, ROMs, and the like.

Program code means comprising one or more program modules may be storedon the hard disk 39, magnetic disk 29, optical disk 31, ROM 24, and/orRAM 25, including an operating system 35, one or more applicationprograms 36, other program modules 37, and program data 38. A user mayenter commands and information into the computer 20 through keyboard 40,pointing device 42, or other input devices (not shown), such as amicrophone, joy stick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit21 through a serial port interface 46 coupled to system bus 23.Alternatively, the input devices may be connected by other interfaces,such as a parallel port, a game port, or a universal serial bus (USB). Amonitor 47 or another display device is also connected to system bus 23via an interface, such as video adapter 48. In addition to the monitor,personal computers typically include other peripheral output devices(not shown), such as speakers and printers.

The computer 20 may operate in a networked environment using logicalconnections to one or more remote computers, such as remote computers 49a and 49 b. Remote computers 49 a and 49 b may each be another personalcomputer, a server, a router, a network PC, a peer device or othercommon network node, and typically include many or all of the elementsdescribed above relative to the computer 20, although only memorystorage devices 50 a and 50 b and their associated application programs36 a and 36 b have been illustrated in FIG. 6. The logical connectionsdepicted in FIG. 6 include a local area network (LAN) 51 and a wide areanetwork (WAN) 52 that are presented here by way of example and notlimitation. Such networking environments are commonplace in office-wideor enterprise-wide computer networks, intranets and the Internet.

When used in a LAN networking environment, the computer 20 is connectedto the local network 51 through a network interface or adapter 53. Whenused in a WAN networking environment, the computer 20 may include amodem 54, a wireless link, or other means for establishingcommunications over the wide area network 52, such as the Internet. Themodem 54, which may be internal or external, is connected to the systembus 23 via the serial port interface 46. In a networked environment,program modules depicted relative to the computer 20, or portionsthereof, may be stored in the remote memory storage device. It will beappreciated that the network connections shown are exemplary and othermeans of establishing communications over wide area network 52 may beused.

Preferably, computer-executable instructions stored in a memory, such ashard disk drive 27, and executed by computer 120 embody the illustratedinference engines, including processes 104, 106 (including processes218, 220) and engines 110, 116. Moreover, computer 20 is suitablyembodies computer 120.

In operation, system 100 transforms disparate data for use in renderingan underwriting decision involving a potentially insurable risk. Theprocesses 104, 106, for example, receive data, which is in a pluralityof formats, from a plurality of sources (i.e., external data 102). Atleast process 106 extracts the data and converts it into one or morestandard formats. The heuristic engine 110 then filters the converteddata by relevancy to the underwriting decision to be rendered. Theconsolidation and presentation engine 116 generates presentableknowledge from the converted data, and presents the knowledge to adecision-making entity for rendering the underwriting decision. Bymonitoring one or more actions on the presented knowledge by thedecision-making entity, optimization process 122 can adjust one or moreof steps as a function of the monitored actions.

Alternatively, in operation, system 100 structures and transformsdisparate data for use in rendering an underwriting decision involving apotentially insurable risk. The processes 104, 106, for example,retrieve data from a first database, such as database 102, and transformthe retrieved data into domain-specific information. Once transformed,the information, which relates to the potentially insurable risk, isstored in a second database, such as staging area repository 108. Theheuristic engine 110 defines one or more relevancy factors as a functionof the underwriting decision to be rendered and assigns at least one ofthe relevancy factors to at least a portion of the information stored inthe second database. Additionally, consolidation and presentation engine116 providing an output of the second database with the assignedrelevancy factors to a decision-making entity for rendering theunderwriting decision.

The order of execution or performance of the operations in embodimentsof the invention illustrated and described herein is not essential,unless otherwise specified. That is, the operations may be performed inany order, unless otherwise specified, and embodiments of the inventionmay include additional or fewer operations than those disclosed herein.For example, it is contemplated that executing or performing aparticular operation before, contemporaneously with, or after anotheroperation is within the scope of aspects of the invention.

Embodiments of the invention may be implemented with computer-executableinstructions. The computer-executable instructions may be organized intoone or more computer-executable components or modules. Aspects of theinvention may be implemented with any number and organization of suchcomponents or modules. For example, aspects of the invention are notlimited to the specific computer-executable instructions or the specificcomponents or modules illustrated in the figures and described herein.Other embodiments of the invention may include differentcomputer-executable instructions or components having more or lessfunctionality than illustrated and described herein.

When introducing elements of aspects of the invention or the embodimentsthereof, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

Having described aspects of the invention in detail, it will be apparentthat modifications and variations are possible without departing fromthe scope of aspects of the invention as defined in the appended claims.As various changes could be made in the above constructions, products,and methods without departing from the scope of aspects of theinvention, it is intended that all matter contained in the abovedescription and shown in the accompanying drawings shall be interpretedas illustrative and not in a limiting sense.

What is claimed is:
 1. A method of structuring and transformingdisparate data for use in rendering a decision involving a potentiallyinsurable applicant, said method comprising: retrieving, by a computer,data from a first database, said retrieved data relating to theapplicant; and executing, by the computer, computer-executableinstructions for: transforming the retrieved data into domain-specificinformation, said domain-specific information relating to insurabilityof the applicant; storing the transformed domain-specific information ina second database; defining one or more relevancy factors as a functionof the decision to be rendered involving the applicant; assigning atleast one of the relevancy factors to at least a portion of thetransformed domain-specific information stored in the second database;processing the transformed domain-specific information as a function ofthe assigned relevancy factors; providing an output comprising theassigned relevancy factors to an underwriter for use in rendering thedecision involving the applicant, wherein the processing, by thecomputer, comprises at least one of: ant colony optimization, aheuristic algorithm, network theory, predictive modeling, deterministicchaos, behavioral economics, fractal geometry, and cellular automata;monitoring one or more actions on the provided output by the underwriterin rendering the decision; generating feedback as a function of themonitored actions; adjusting, by the computer, at least one of saidtransforming, defining, assigning, and processing the transformeddomain-specific information subsequent to providing the feedback to theunderwriter; and presenting an updated output to the underwriter basedon the feedback.
 2. The method of claim 1, wherein transforming theretrieved data into domain-specific information comprises executing adomain-specific Extract, Transform, Load (ETL) process to extract theretrieved data and convert the extracted data into one or more standardformats.
 3. The method of claim 1, wherein the data stored in the firstdatabase comprises one or more of the following types of data:applicant-provided data, electronic medical records data, electronichealth records data, continuity of care records data, prescription data,other medical sources data, financial sources data, motor vehiclerecords data, and other non-medical sources data.
 4. The method of claim1, wherein assigning the at least one of the relevancy factors comprisesexecuting a heuristic engine on the information stored in the seconddatabase to infer risk assessment relationships among the information.5. The method of claim 1, wherein the retrieved data comprises one ormore of the following types of complex data: social network data anddatamart data.
 6. The method of claim 5, further comprising executing adata mining process on the complex data to identify covariancerelationships among the data.
 7. The method of claim 6, wherein the datamining process comprises predictive modeling.
 8. The method of claim 1,wherein one or more computer-readable media have computer-executableinstructions stored thereon for performing the method of claim
 1. 9. Asystem of structuring and transforming disparate data for use inrendering a decision involving a potentially insurable applicant, saidsystem comprising: a first database storing data relating to theapplicant; and a computer configured to execute computer-executableinstructions for: transforming data retrieved from the first databaseinto domain-specific information, said domain-specific informationrelating to insurability of the applicant; storing the transformeddomain-specific information in a second database; defining one or morerelevancy factors as a function of the decision to be rendered involvingthe applicant; assigning at least one of the relevancy factors to atleast a portion of the transformed domain-specific information stored inthe second database; processing the transformed domain-specificinformation as a function of the assigned relevancy factors; providingan output comprising the assigned relevancy factors to an underwriterfor use in rendering the decision involving the applicant, wherein theprocessing comprises at least one of: ant colony optimization, aheuristic algorithm, network theory, predictive modeling, deterministicchaos, behavioral economics, fractal geometry, and cellular automata;monitoring one or more actions on the provided output by the underwriterin rendering the decision; generating feedback as a function of themonitored actions; adjusting, by the computer, at least one of saidtransforming, defining, assigning, and processing the transformeddomain-specific information subsequent to providing the feedback to theunderwriter; and presenting an updated output to the underwriter basedon the feedback.
 10. The system of claim 9, wherein transforming theretrieved data into domain-specific information comprises executing adomain-specific Extract, Transform, Load (ETL) process to extract theretrieved data and convert the extracted data into one or more standardformats.
 11. The system of claim 9, wherein the data stored in the firstdatabase comprises one or more of the following types of data:applicant-provided data, electronic medical records data, electronichealth records data, continuity of care records data, prescription data,other medical sources data, financial sources data, motor vehiclerecords data, and other non-medical sources data.
 12. The system ofclaim 9, wherein assigning the at least one of the relevancy factorscomprises executing a heuristic engine on the information stored in thesecond database to infer risk assessment relationships among theinformation.
 13. The system of claim 9, wherein the data retrieved fromthe first database comprises one or more of the following types ofcomplex data: social network data and datamart data.
 14. The system ofclaim 13, wherein the computer is further configured to executecomputer-executable instructions for executing a data mining process onthe complex data to identify covariance relationships among the data.15. The system of claim 14, wherein the data mining process comprisespredictive modeling.
 16. A non-transitory computer-readable mediumstoring computer-executable instructions, which instructions whenexecuted by a computer cause the computer to structure and transformdisparate data for use in rendering a decision involving a potentiallyinsurable applicant, said computer-executable instructions comprising:transforming data relating to the applicant into domain-specificinformation, said domain-specific information relating to insurabilityof the applicant; defining one or more relevancy factors as a functionof the decision to be rendered involving the applicant; assigning atleast one of the relevancy factors to at least a portion of thetransformed domain-specific information; processing the transformeddomain-specific information as a function of the assigned relevancyfactors; providing an output comprising the assigned relevancy factorsto an underwriter for use in rendering the decision involving theapplicant, wherein the processing comprises at least one of: ant colonyoptimization, a heuristic algorithm, network theory, predictivemodeling, deterministic chaos, behavioral economics, fractal geometry,and cellular automata; monitoring one or more actions on the providedoutput by the underwriter in rendering the decision; generating feedbackas a function of the monitored actions; adjusting at least one of saidtransforming, defining, assigning, and processing the transformeddomain-specific information subsequent to providing the feedback to theunderwriter; and presenting an updated output to the underwriter basedon the feedback.
 17. The computer-readable medium of claim 16, whereintransforming the retrieved data into domain-specific informationcomprises executing a domain-specific Extract, Transform, Load (ETL)process to extract the data and convert the extracted data into one ormore standard formats.
 18. The computer-readable medium of claim 16,wherein the data comprises one or more of the following types of data:applicant-provided data, electronic medical records data, electronichealth records data, continuity of care records data, prescription data,other medical sources data, financial sources data, motor vehiclerecords data, and other non-medical sources data.
 19. Thecomputer-readable medium of claim 16, wherein assigning the at least oneof the relevancy factors comprises executing a heuristic engine on thetransformed domain-specific information to infer risk assessmentrelationships among the information.
 20. The computer-readable medium ofclaim 16, wherein the data comprises one or more of the following typesof complex data: social network data and datamart data.
 21. Thecomputer-readable medium of claim 20, wherein the computer-executableinstructions further comprise executing a data mining process on thecomplex data to identify covariance relationships among the data. 22.The computer-readable medium of claim 21, wherein the data miningprocess comprises predictive modeling.